Become a Creator today!Start creating today - Share your story with the world!
Start for free
00:00:00
00:00:01
#121 - Peter van Kersen | Streamlining HR for Startups
6 Plays23 minutes ago
Peter is a seasoned People/HR Leader with a track record of aiding startups in achieving sustainable and rapid growth. He emphasizes the significance of establishing a strong foundation, likening it to building a structure. Having contributed to the expansion of companies such as Catawiki, Delivery Hero, Glovo, REMY Robotics, and Docplanner, Peter excels in fostering empowering experiences and solid frameworks. His proficiencies encompass diverse areas like People analytics, Business unit management, Talent development, HR Operations, Digital Transformation, Performance management, Compensation & Benefits, and Leadership growth.
Shownotes
00:00 - The Challenge of Disconnected HR Systems
03:11 - Navigating HR Tools for Startups
05:58 - Data Flow and Workforce Planning
09:08 - Streamlining Data Collection for New Hires
12:02 - Integrating Systems for Efficient HR Operations
15:00 - Leveraging Data for People Analytics
18:02 - The Importance of Macro and Micro Data
20:59 - AI in Recruitment and Performance Management
24:00 - Building a Comprehensive Hiring Framework
27:00 - Connecting Startups with HR Solutions
Links
Peter Linkedin https://www.linkedin.com/in/petervankersen/
Thomas Linkedin: https://www.linkedin.com/in/thomas-kohler-pplwise/
Thomas e-mail: thomas@pplwise.com
pplwise: https://pplwise.com/
Recommended
Transcript
Challenges in HR System Integration
00:00:08
Speaker
Today we have Peter here again. And Peter and I had a conversation on just how we're both doing. And you also now fully started running your own business, right? And you also scaled it a bit.
00:00:22
Speaker
And um we talked about one problem that we both still see and face, and that ah HR systems are not talking to each other properly, are not communicating to each other properly.
00:00:33
Speaker
And there are a lot of implications out of did it. um One, you cannot really... um rely on the data you have. You cannot really get efficient. You also cannot really use AI um at some point, maybe if you want to implement it as a business, if the foundation is not there, right? So there are a lot of implications. We will talk through them and how to anticipate them.
Supporting Startups with HR Management
00:00:52
Speaker
But maybe first, Peter, um give us an update on um what's happening on your side um personally or career-wise. And then let's go a bit into the topic. Yeah, sure.
00:01:06
Speaker
and So my my company, Working Capital, we we partner with generally startups to take over their HR team or to be their HR team. um That usually involves and some recruiting, it involves some admin, and it involves an HR manager or an HR business partner.
00:01:25
Speaker
and And the one thing we see with every company out there and that's just getting started, that probably raised some money and is and is ready to scale, is that they struggle with the the hr tools ah they they they they don't have the time to invest in in researching what the best tool is for them so they generally go with um an ats that a quick google search has shown them is the best in class or best in the market um and an hras that will fit their their scale and growth plans and and the issues that we then see is that these systems don't talk to each other um
00:02:02
Speaker
And once they start using the HRAS and it turns out that the engagement module is lacking, it's not quite what they need. They get an engagement tool and and they want to add performance management. So they add a performance management tool.
00:02:15
Speaker
And before you know it, you have five or six or seven different tools that all don't talk to each other or and or do so on a very limited ah basis. So that's um I think we're uniquely positioned to recognize these
Prioritization and Integration of HR Tools
00:02:30
Speaker
issues.
00:02:30
Speaker
and now we've also tried ah start to fix them. and But the reality is that there's still nothing out there that really fits the bill and ah to to make this the ecosystem that we all want.
00:02:43
Speaker
And what are maybe some, what is the company stage you you usually see this problem happening? So we we generally work with companies that are pre-Series A or just post-Series A, so around 30 employees,
00:03:01
Speaker
up to 300 or even larger. um So about to go on the journey of establishing a people function, and but not quite established yet.
00:03:15
Speaker
And um what systems do they usually already have in place or want to implement? And what's the order?
00:03:22
Speaker
Yeah, it's it's always the same. and It generally starts with an ATS. Once a company reaches 30 employees, they're still doing most of their ah HR in Google Sheets or in Excel.
00:03:33
Speaker
and Or they they have an accountant that and that also manages payroll and has and as most of the employee data. and So then on on the minds of most CEOs is hiring.
00:03:45
Speaker
So they get an a recruiter and an ATS. so and And then later on, they figure out that it will be very useful to have all the employee data in one place. And they invest in an HRAS. And um when is the performance um management tools or the engagement tools, when are they kicking in?
00:04:05
Speaker
Generally later, which is strange to me because it's it's not like after you've reached 100 employees or 200 employees and all of a sudden ah performance or engagement become important. I think they're important regardless of what stage the company is in because you still have employees and they still need to know themselves and they still need to grow.
00:04:23
Speaker
um But generally we see it as a, as an, as an extra, as a next phase in the, in the progression. So let's say around a hundred employees, um, they'll start thinking about sending out an engagement survey or doing a performance reviews.
00:04:36
Speaker
Okay. And then you have four systems.
00:04:42
Speaker
Yeah. Well, but let's imagine that, um, we're, we're a tech company and we also want to test, uh, some of the developers that we interview. Now we have a test tool that we hook onto the, yeah to the ATS.
00:04:55
Speaker
Or um but we want to send out contracts that we want an e-signature for. it Now we have an e-signature
Data Transfer Inefficiencies in HR
00:05:01
Speaker
tool. And you can you can see how this quickly starts to add up.
00:05:06
Speaker
Or you need to source candidates and then you have a sourcing tool. Or you need to schedule interviews and then you have a scheduling tool. Exactly. Okay. And um what limitations do you see when you have...
00:05:21
Speaker
different tools decentralized that actually um contribute to a to to one process or to one experience maybe. Yeah.
00:05:34
Speaker
So the main limitation we see is then is the way data moves around this ecosystem and and what the source of truth is.
00:05:45
Speaker
So I remember the last time we spoke, we spoke about workforce planning and yes and where and Where roles originate, and and we can go back to that example.
00:05:57
Speaker
So if you have a workforce planning tool or if you have the need for for a position, so you and for example, yeah, in an Excel, um there's there's certain characteristics about that position that then get transferred into the ATS if you're lucky and will also need to reach the HRAS at some point.
00:06:16
Speaker
and And that piece of data... What could that be? Let's say we are coming from a budget. yeah Because usually headcount plan is done in a budget. And then um the budget says, okay, a certain...
00:06:29
Speaker
number of positions need to start at a certain period of time a certain amount of costs. um What's often not getting transferred into the ATS is what should be start date, what should then be the hiring date, so offer signed date for instance, and then when do we actually need to kick off the role.
00:06:57
Speaker
And what activities do we need to plan from a recruiting perspective in order to get the hire done and the start date filled, right? um I think... the These are the basic things that are in the budget that are not transferred to the recruiting system.
00:07:14
Speaker
Just we need to, we we got approval for this role hire for it. yeah That's it. Right? Yeah. And then what's what's happening then, but let's say the hire is done. Usually maybe delayed.
00:07:26
Speaker
A few u were maybe not delayed, but let's say two-thirds always delayed because planning is not accurate or not aligned. And then it's, the hire is done, what's happening then usually is a limitation. You send out the DocuSign contract or whatever tool you use and then the hire is marked in Ashby, Greenhouse, Lever, Smart Recruiters, whatever you want to use and what's happening then. Yeah, yeah exactly. this is so So now we've reached this point where there are, i always try to think of this as packages of data.
00:07:59
Speaker
So the first package of data comes from an Excel sheet that we call workforce planning and it has, and the team, the department, the division, the the the salary range, the type the the name of the role.
00:08:10
Speaker
Maybe the the name of the role is different as the job title. So these are kind of objective data that originated somewhere. At some point, a person is going to fill this role and they have a name and it date a date of birth and an address and all things that are unique to this person.
00:08:26
Speaker
But we need this in order to send out an offer letter. Most of this information we already get from the applicant once they've applied or when the recruiters has has has put it into the system.
00:08:38
Speaker
and But after the offer has been accepted, we also need to create a contract. They need to have hardware. So we need to know what kind of hardware they want. and We need the actual start date because it's probably different.
00:08:51
Speaker
And then we need a bunch of information like in Germany, social security number, the tax ID, and how many kids you have, the birth dates of your kids. and And this...
00:09:01
Speaker
let's call it package number three, is all the stuff that we need to not just create a contract, but also enroll someone with social security. And in Germany, this
Automation in HR Data Management
00:09:10
Speaker
is quite complex, but you should see how it is in Brazil, for example, where we also need to know if someone is a military veteran, and what their blood type is, et cetera. So this package number three gets extremely complex.
00:09:23
Speaker
And this is something that ATSs and HRSs have not figured out yet. So how to come gather this in a structured way so that it takes the minimum amount of effort and manual work for an HR manager to gather this and process it.
00:09:40
Speaker
So this is just one of of the frustrations of how information doesn't move around and in um in a smooth way for them to be used by the HR operations team.
00:09:51
Speaker
In a not even big company. Exactly. Yeah. It doesn't matter how big you are as a company, as a startup in Germany, we still need to gather this information. And I've seen so many teams do this via email and then copy paste all the information into a system.
00:10:11
Speaker
And how do you solve this now? So unfortunately through a workaround, yeah so the, the, one of the latest clients I work with at the greenhouse,
00:10:22
Speaker
Bamboo HR setup, which is very common. and Bamboo and and Greenhouse have a native integration, but it doesn't cover for any of of the things that we just discussed in package number three. um So what we do is so ah we've written a script that gathers to the data from Greenhouse, which should remain um constant because it's let's it's, let's call it package number one, it's everything that was in the workforce planning sheet, the the team, the department, etc. Then we then we gather package number two, which is all the candidate information that they've already given when they applied.
00:10:58
Speaker
and And then we send them a form for package number three. So all of the information that we need to get them a contract and get them enrolled with social security. and And once, so we we have that all scripted together in a Google sheet.
00:11:13
Speaker
And from there, we can start doing things with Zapier or make.com or N8N. and to transform that data into the documents that we actually need and to and to get it into the system.
HR Analytics and Data Literacy
00:11:23
Speaker
So from common database like Airtable, and it's very it's relatively simple now with these types of tools to create a package of data that consists of everything we just discussed and to then upload it yeah into your HRAS.
00:11:42
Speaker
Okay, and and then you build integrations or um custom, um how do you call it, like API calls or push-pull requests that take data, gather data and then write it into your sheet.
00:12:02
Speaker
And then from there, it transfers it to the right tool with the right information. And this is all all automated. Exactly. Yeah, there's there's a few different ways to do it. and One is to create a central database that has all of the information.
00:12:16
Speaker
and Another is to and create webhooks in one of your systems that then trigger the other system to go and grab whatever information they need, which is a little cleaner, but either works. so The benefit of having a central database that has all this information is that you can then also um do other stuff with it. So for example, in in Germany,
00:12:39
Speaker
and We need not just a contract, but also a few other documents that that need to be created and signed when someone joins a company. um This can all then be created outside of the HRAS or if the HRAS doesn't have um this capability.
00:12:57
Speaker
Mm-hmm. Yeah, interesting. and And you could then also do a lot of analysis with it, right, without just processing work steps from, um and would say, a default perspective. So what are the things that are necessary that you have to do in order to hire somebody or um prepare everything for payroll.
00:13:20
Speaker
um What else can you do with the data? I think you can also do a lot of um reports, in lights insights, right? Because I think the whole people analytics function or idea is also based on data literacy.
00:13:36
Speaker
It is. um like I'm just, so I i think that then all the insights that you could, Yeah, all the useful insights are probably, you you probably wouldn't want to use this data set for, because it's it's um so for two reasons. One is, it's literally the stuff that you wouldn't be able to draw much information from.
00:14:03
Speaker
and What's your name, address, location, etc. yeah um And the second is, it's going to end up in the HRS anyway. So if you're going to get any type of insights, I would rather do it from the than from the raw data set that serves as an in-between to match everything.
00:14:19
Speaker
and I think what is very interesting is something that you could do later on. so And it's part of the problem. And let me elaborate a little bit. So where insights become useful is if we start to get to know you as an employee a little bit further down the line.
00:14:38
Speaker
Actually, that's not true because we also have information from the recruitment system, which we almost never use. right? It's often gathered. We spend hours interviewing you, making notes, filling scorecards, and then we kind of forget about you at the three-month mark or the six-month mark or after your first performance review, right? it's it's Most companies will not look at the data that we've gathered during the recruitment process when they do your first performance so and review, which is crazy. and and i So how we could do that or how we could
00:15:11
Speaker
make use of all of those sets so of data is to gather it in yet a third data source. um Ideally, that would be the HRS, but I don't think there's an HRS out there that now gathers all this information about your current performance, and how many sales you make, and what your engagement is, et cetera.
00:15:27
Speaker
So I know a lot of companies do that either in um an analytics tool and or in a data warehouse and then build something on top with a Tableau or Power BI, et cetera.
00:15:42
Speaker
um That's interesting because I also experiment a lot with this, especially in your recruiting side. So there are several aspects what I look at first. What is the macro information? And then I would say also the more detailed information. So macro information for me would be...
00:15:59
Speaker
um Let's say and ah company wants to enter Germany, then they start building up a sales function. And then um we need to first understand, okay, what's maybe their revenues?
00:16:12
Speaker
What is their ideal customer profile? What's the annual contract value they're selling? um What is the sales cycle in other regions? um What maybe customers do they already have? What infrastructure do they already have? In what um period or phase are there in terms of product market fit? Is it something that tests new or is it just something they they tried to scale and already have um experience with?
00:16:36
Speaker
This is the macro meta information, right? And then we can filter based on candidates we already interviewed. um What are candidates expecting?
00:16:50
Speaker
and what is necessary in order of recruiter input. So how many reach outs, how many screenings calls are necessary. In order to hire somebody from 50 million ARR company that um has um sales targets of 800 to 1.2 million on annual sales per year for a seller, where the product is easy to sell just from a...
00:17:20
Speaker
um data perspective in terms of the recruit the sellers we interview say it's four out of five easy to sell this product versus maybe an early stage company would answer a seller would answer it's one out of five because the company doesn't the product doesn't exist yet right so you cannot compare this plus also um what is the average target attainment within the team within the company if 100% of the sellers reach the target Wow, great.
00:17:51
Speaker
But if we interview sellers from companies that just reach 60% of the targets, um we might can also understand, okay, why is it maybe company maturity? Is it um just that they are not set up with a good sales management?
00:18:05
Speaker
um Do they have maybe too much sellers and too small territories and so on? um And these are all informations what I try to gather and mix up from a macro and micro perspective, right, that we just interview And you can now have transcripts or not transcripts. You can have um and note takers with a certain template of questions you ask.
00:18:27
Speaker
And then when you get the answer, you get the transcript and the transcript can can be transferred with an API towards a database. And then you can filter for what is actually, what what are the recruiters talking to?
00:18:39
Speaker
um actually, but what data are they generating, right? And then how can you filter that? Because I cannot just filter transcripts. I need to and manipulate the data um in a way that it's filterable, right? And that you can do requests from it. So this is super interesting. And then the second piece is the hires we made, for instance, for companies, um we tracked them down and then ultimately said, okay, how many interviews um on each step were needed in order to make this hire?
00:19:12
Speaker
checking in after six to 12 months, is the person still there? um Did they maybe get a promotion? um Then also trying to check in and say, hey,
00:19:24
Speaker
Do you reach your targets? How is it? And we are still in the beginning of doing this because this just takes time, right? You need to do, we probably made around 1,500 hires for customers over the past four years.
00:19:36
Speaker
And of course, you don't remember everything because I just started roughly a year ago doing this and so on. And then it just takes time building up this data. But once we have this data, um you can enrich it with so much matter information that um this is so valuable. and but the the foundation is that the data is correct and that, um, you just can make sure that you also are gathering data in a reliable way all the time. Right.
00:20:03
Speaker
Yeah. Yeah.
Integrating Recruitment and Performance Data
00:20:04
Speaker
Yeah. I think, I think this is fascinating and I think this is, um, going to be one of the big breakthroughs in, in recruitment and NHR in general in the next four or five years.
00:20:16
Speaker
Um, and I think what so you've said a few things that are extremely interesting to me. And and one is, um, It's very simple. You cannot remember everything. So by by transcribing every single interview and having an AI go through it and synthesize it and find common themes.
00:20:33
Speaker
um And put it in a database format. Put it in a database and and and have it analyzed. It already saves probably thousands of hours of a human going through conversations and finding patterns.
00:20:48
Speaker
but this this The AI is fantastic at finding patterns. ah better much much better than any a human could um and and now that we have those things both on a meta level like you said um what what kind of trends do we see but also on an individual level how do we get to know ourselves and the people that we work with how do we get to know our sales and people and um how do we make sure that they develop throughout their life cycle and and and better yet so let's imagine that so that you would you could call this an agent
00:21:21
Speaker
and you You have something, um transcribe all of your interviews, put it in a database, give it context, and and then deliver an output. and Imagine that that agent could talk to the agent that goes through all the performance reviews six months from now. So rather than having a quality of higher metric that is, is this person still here, yes or no, after 12 months, you could have the actual sales data and what type of how they do conversations, how they do sales.
00:21:51
Speaker
um so and and what makes them better at this than then the next person? And can we correlate that with the information that we got from the from the hiring agent? And I think then we have an incredibly powerful system that not just assesses who your best salespeople are, but why, and where they may need some training or guidance or just need to be put on a different product to sell because their style is not what we need in this company.
00:22:19
Speaker
Mm-hmm. um Peter, you actually just described what I'm trying to build, right? So, um, I'll show you the concept here. So you have the pre-hiring, hiring, and post-hiring stage. And then you get maybe data from headcount planning and budgeting.
00:22:36
Speaker
You get data from kickoff meetings. It's a document, whatever. It's Notion, it's Word, it's Docs. Doesn't matter. um Then you reach out. It's LinkedIn, it's GitHub, it's email, it's even WhatsApp. It's sometimes cold calls, whatever it is. But you can somehow...
00:22:55
Speaker
now track and connect um the systems um that then streamline the data. Then you have the ATS. Then you have the, let's say, interview data from, i just now put some some few examples in MetaView. There are also other tools, right, where you can just record calls and transcribe.
00:23:13
Speaker
um Then you have calendar data. um How many interviews does a recruiter or hiring manager have um in their, calendar what other meetings do they have and do they prioritize hiring right this could be displayed by this because it's often a problem um then also what's then in the offer letter is and and what what did the candidate say in the interview what is then in the ats And what is then offer letter ultimately, right?
00:23:45
Speaker
How many candidates are gambling salary expectations? This is what you could read from this. um And then also when the hire is done, um satisfaction metrics like PECAN or also performance metrics from culture and whatever it is, right? And then what I leave left blank is the quality of hire.
00:24:03
Speaker
And I just um put in already some mock-up data. So this is not real data, but you could display. For instance, you click on AI software engineer or a sales manager, and then you have one company.
00:24:15
Speaker
I put in tax fix there because I just um showed it to to them um how this could look like and I worked with them. And then they could see, for instance, What are their current metrics? Because um you could just plug in um the technology with all the tools, connect the systems.
00:24:30
Speaker
And then um we could then also say, okay, what other customers that are similar, for instance, to this company TaxFix, let's say Series D, 500 employees, and certain amount of revenue, a certain amount of um markets. And then you benchmark this with also maybe other fintechs or with other companies at Series D um or whatever you want a benchmark it against. And then you get...
00:24:55
Speaker
a benchmark and then you could see, okay, is it better or worse than benchmark? And this is all just and fictional data, right? So we did not use this yet. um But this is what I'm experimenting with and I'm starting step by step with using interview data and then generating actually benchmarks um that showcase I would say more sales management data, what it maybe a VP sales or a CRO or a CEO want to want to see, right?
00:25:24
Speaker
What is the collaboration between sales performance and candidates we hire from, right? Because I think that's currently the most relevant um and the easiest to do because we we own a lot of this data because we generate a lot of interviews and we we we do a lot of things um on on on our systems right i don't have access to the hr systems of our customers and so on and so forth and to the budget plans um so step five right but the concept and the idea is actually also similar to what what you're thinking i'm just tackling it from a recruiting perspective yeah yeah yeah and it it makes it makes complete sense um and i yeah um i i i can imagine why that's that's
00:26:08
Speaker
um, the conclusion that you would like to draw, right? Use,
Overcoming Bias in Recruitment Insights
00:26:11
Speaker
uh, this model because the, I think you called it the total value of a placement, um, is, is much higher total lifetime placement than you would, uh, doing it yourself.
00:26:22
Speaker
And i think this is a fascinating way of, do of thinking about it. Um, but what interests me much more as a, as a, as a CPO or someone working with, um, uh, setting up people department is, um,
00:26:36
Speaker
How can we make, how can we get to know people and make sure that they're in the right and place? And one of, one of the issues I see is that we still do things. So the biggest issue is the missing data.
00:26:53
Speaker
So when you hire someone, you've, they've already gone through a selection funnel and we already have certain expectations of them. Otherwise we wouldn't hire them. So if we want to do a proper AB b test, we we should just hire,
00:27:06
Speaker
anyone, everyone who applies and then see what the performance metrics are. So what I guess I'm trying to say is ah everyone we gather performance data on at some point during their career is already skewed because we already had certain expectations. So, um, we, we, we experimented a few years ago with, um, uh, giving people from a personality test, uh, personality and aptitude tests.
00:27:35
Speaker
during the interview process. and And what happened is we only started hiring people who scored highly on aptitude for a certain role and and who we thought the personalities would match best with a sales or a customer success role.
00:27:48
Speaker
and But the issue was that we only selected for people who already scored highly in those things. So the the spread that we had ah measuring performance was extremely small and and we couldn't really we couldn't really get any um any useful data out of this.
00:28:04
Speaker
and And I think this is where and AI that can go through millions of data points or at least thousands of data points will be able to help and and and and predict a lot better which people will be successful in a role and and ultimately will be happier in a role because so and let's face it,
00:28:24
Speaker
um it's difficult to be happy in a role if you're not successful. and and yeah And I think but thinking about the the problem, like the way you just showed, is going to be a massive step in the right direction.
00:28:38
Speaker
and So how
Contact Information for HR Solutions
00:28:39
Speaker
can startups that don't have the right people function or infrastructure yet and reach out to you from c to Series C? how How do they reach you best?
00:28:49
Speaker
ah LinkedIn is easy. Or send me an email. Peter at workingcapitalou.com. dot com But if you connect with me on LinkedIn and shoot me a message, and that's easiest. Then we also link um your LinkedIn in the show notes and um you will also be tagged on the LinkedIn videos.
00:29:06
Speaker
Perfect. Great. Thanks. No, thank you. always Always a pleasure to to talk to you.